ABSTRACT
Nowadays, machine learning (ML) is used in several applications, including statistics, data processing, healthcare, stock market, etc. ML can be applied to different types of medical data (structured and unstructured data). Distinguishing unhealthy from healthy groups improves decision making in clinics. The given dataset is categorized into different classes based on ML techniques. A classification pattern generates a conclusion from the observed data. During the training task, most of the classifiers learn from the given dataset. ML techniques, achieving high rates of successful predictions, have attracted the attention of medical practitioners and researchers. The corresponding manual procedure takes more time, is inconsistent, and depends on expert decision making. Automatic classification plays a major role in the medical field. In the information technology method, the computer-aided design (CAD) tool has been adopted to assist medical practitioners to diagnose disease. CAD is most commonly used for classification problems, where a label is assigned, based on a number of features. ML automatically finds a solution from the patient’s data and improves care in a cost-effective manner in healthcare management. The ML classifier gives a decision from the data which further reduces the work of medical practitioners, with the treatment being carried out in a shorter time. ML has been used to track diseases, such as COVID-19, SARS, tumors, etc. In this chapter, different ML classifiers used in health care and their use in case studies will be discussed.
